scholarly journals Using Primary Reinforcement to Enhance Translatability of a Human Affect and Decision-Making Judgment Bias Task

2021 ◽  
pp. 1-13
Author(s):  
Vikki Neville ◽  
Peter Dayan ◽  
Iain D. Gilchrist ◽  
Elizabeth S. Paul ◽  
Michael Mendl
Keyword(s):  
Decision Making ◽  
Affective State ◽  
Lapse Rate ◽  
Prediction Errors ◽  
Affective Valence ◽  
Behavioral Measures ◽  
Judgment Bias ◽  
Affective Arousal ◽  
Nonhuman Animals ◽  
Positive Valence

Abstract Good translatability of behavioral measures of affect (emotion) between human and nonhuman animals is core to comparative studies. The judgment bias (JB) task, which measures “optimistic” and “pessimistic” decision-making under ambiguity as indicators of positive and negative affective valence, has been used in both human and nonhuman animals. However, one key disparity between human and nonhuman studies is that the former typically use secondary reinforcers (e.g., money) whereas the latter typically use primary reinforcers (e.g., food). To address this deficiency and shed further light on JB as a measure of affect, we developed a novel version of a JB task for humans using primary reinforcers. Data on decision-making and reported affective state during the JB task were analyzed using computational modeling. Overall, participants grasped the task well, and as anticipated, their reported affective valence correlated with trial-by-trial variation in offered volume of juice. In addition, previous findings from monetary versions of the task were replicated: More positive prediction errors were associated with more positive affective valence, a higher lapse rate was associated with lower affective arousal, and affective arousal decreased as a function of number of trials completed. There was no evidence that more positive valence was associated with greater “optimism,” but instead, there was evidence that affective valence influenced the participants' decision stochasticity, whereas affective arousal tended to influence their propensity for errors. This novel version of the JB task provides a useful tool for investigation of the links between primary reward and punisher experience, affect, and decision-making, especially from a comparative perspective.

scholarly journals The Impact of Visual Art and High Affective Arousal on Heuristic Decision-Making in Consumers

2020 ◽  
Vol 11 ◽  
Author(s):  
Yaeri Kim ◽  
Kiwan Park ◽  
Yaeeun Kim ◽  
Wooyun Yang ◽  
Donguk Han ◽  
...  
Keyword(s):  
Decision Making ◽  
Visual Arts ◽  
Stroop Task ◽  
Visual Art ◽  
Abstract Art ◽  
Cognitive Resources ◽  
Affective Valence ◽  
Product Packaging ◽  
Affective Arousal ◽  
The Impact

In marketing, the use of visual-art-based designs on products or packaging crucially impacts consumers’ decision-making when purchasing. While visual art in product packaging should be designed to induce consumer’s favorable evaluations, it should not evoke excessive affective arousal, because this may lead to the depletion of consumer’s cognitive resources. Thus, consumers may use heuristic decision-making and commit an inadvertent mistake while purchasing. Most existing studies on visual arts in marketing have focused on preference (i.e., affective valence) using subjective evaluations. To address this, we applied a neuroscientific measure, electroencephalogram (EEG) to increase experimental validity. Two successive tasks were designed to examine the effects of affective arousal and affective valence, evoked by visual artwork, on the consecutive cognitive decision-making. In task 1, to evaluate the effect of visual art, EEG of two independent groups of people was measured when they viewed abstract artwork. The abstract art of neoplasticism (AbNP) group (n = 20) was showing Mondrian’s artwork, while the abstract art of expressionism (AbEX) group (n = 18) viewed Kandinsky’s artwork. The neoplasticism movement strove to eliminate emotion in art and expressionism to express the feelings of the artist. Building on Gallese’s embodied simulation theory, AbNP and AbEX artworks were expected to induce lower and higher affect, respectively. In task 2, we investigated how the induced affect differentially influenced a succeeding cognitive Stroop task. We anticipated that the AbEX group would deplete more cognitive resources than AbNP group, based on capacity limitation theory. Significantly stronger affect was induced in the AbEX group in task 1 than in the AbNP group, especially in affective arousal. In task 2, the AbEX group showed a faster reaction time and higher error rate in the Stroop task. According to our hypotheses, the higher affective arousal state of the AbEX group might deplete more cognitive resources during task 1 and result in poorer performance in task 2 because affect impacted their cognitive resources. This is the first study using neuroscientific measures to prove that high affective arousal induced by visual arts on packaging may induce heuristic decision-making in consumers, thereby advancing our understanding of neuromarketing.

Intra-individual Associations of Perceived Stress, Affective Valence, and Affective Arousal with Momentary Cortisol in a Sample of Working Adults

2021 ◽  
Author(s):  
Jillian A Johnson ◽  
Matthew J Zawadzki ◽  
Dusti R Jones ◽  
Julia Reichenberger ◽  
Joshua M Smyth
Keyword(s):  
Perceived Stress ◽  
Daily Life ◽  
Saliva Sample ◽  
Affective States ◽  
Affective Valence ◽  
Working Adults ◽  
Negative Valence ◽  
Affective Arousal ◽  
Positive Valence ◽  
Valence And Arousal

Abstract Background Research pairing ecological momentary assessment (EMA) methodology and ambulatory cortisol during daily life is still rare, as is careful testing of the within-person associations between stress, affect, and cortisol. Using a circumplex approach, we considered both valence and arousal components of affect. Purpose To examine the within-person covariation of momentary cortisol with momentary perceived stress, affective valence, and affective arousal in everyday life. Methods 115 working adults (Mage = 41.2; 76% women; 76% white) completed six EMA surveys per day over 3 days. Each assessment included reports of perceived stress and affect (used to construct indicators of affective valence and arousal), followed by a saliva sample (from which cortisol was assessed). Multi-level models were used to examine the momentary associations between perceived stress, affective valence, affective arousal, and cortisol. Results Moments characterized by higher perceived stress were associated with higher cortisol (p = .036). Affective valence covaried with cortisol (p = .003) such that more positive valence was associated with lower cortisol and more negative valence with higher cortisol. Momentary affective arousal was not related to cortisol (p = .131). When all predictors were tested in the same model, only valence remained a significant predictor of cortisol (p = .047). Conclusion Momentary perceived stress and affective valence, but not affective arousal, were associated with naturalistic cortisol. Cortisol was more robustly associated with affective valence than perceived stress or affective arousal. These findings extend our understanding of how moments of stress and particular characteristics of affective states (i.e., valence but not arousal) may “get under the skin” in daily life.

scholarly journals A causal test of the mechanisms by which affect state biases affective perception

2021 ◽  
Author(s):  
Keith A. Bush ◽  
Clinton D. Kilts
Keyword(s):  
Individual Differences ◽  
Cognitive Processing ◽  
Affective State ◽  
Affective Valence ◽  
Manipulation Check ◽  
Sensory Stimuli ◽  
Goal State ◽  
Affective Perception ◽  
Positive Valence ◽  
Affect Processing

AbstractIn this study, we merged methods from machine learning and human neuroimaging to causally test the role of self-induced affect states in biasing the affective perception of subsequent image stimuli. To test this causal relationship, we developed a novel paradigm in which (n=40) healthy adult participants observed multivariate neural decodings of their real-time functional magnetic resonance image (rtfMRI) responses as feedback to guide explicit regulation of their brain (and corollary affect processing) state towards a positive valence goal state. By this method, individual differences in affect regulation ability were controlled. Attaining this brain-affect goal state triggered the presentation of pseudo-randomly selected affectively congruent (positive valence) or incongruent (negative valence) image stimuli drawn from the International Affective Picture Set. Separately, subjects passively viewed randomly triggered positively and negatively valenced image stimuli during fMRI acquisition. Multivariate neural decodings of the affect processing induced by these stimuli were modeled using the task trial type (state-versus randomly-triggered) as the fixed-effect of a general linear mixed effects model. Random effects were modeled subject-wise. We found that self-induction of a positive affective valence state significantly positively biased the perceived valence of subsequent stimuli. As a manipulation check, we validated affective state induction achieved by the image stimuli using independent psychophysiological response measures of hedonic valence and autonomic arousal. We also validated the predictive fidelity of the trained neural decoding models for brain states induced by an out-of-sample set of image stimuli. Beyond its contribution to our understanding of the neural mechanisms that bias affect processing, this work demonstrated the viability of novel experimental paradigms triggered by pre-defined affective cognitive states. This line of individual differences experimentation potentially provides scientists with a valuable tool for causal exploration of the roles and identities of intrinsic cognitive processing mechanisms that shape our perceptual processing of sensory stimuli.

scholarly journals Dissecting the links between reward and loss, decision-making, and self-reported affect using a computational approach

2021 ◽  
Vol 17 (1) ◽  
pp. e1008555
Author(s):  
Vikki Neville ◽  
Peter Dayan ◽  
Iain D. Gilchrist ◽  
Elizabeth S. Paul ◽  
Michael Mendl
Keyword(s):  
Decision Making ◽  
Prediction Errors ◽  
Affective States ◽  
Affective Valence ◽  
Judgement Bias ◽  
Decision Outcomes ◽  
Loss Experience ◽  
Average Earning ◽  
Task Experience ◽  
New Evidence

Links between affective states and risk-taking are often characterised using summary statistics from serial decision-making tasks. However, our understanding of these links, and the utility of decision-making as a marker of affect, needs to accommodate the fact that ongoing (e.g., within-task) experience of rewarding and punishing decision outcomes may alter future decisions and affective states. To date, the interplay between affect, ongoing reward and punisher experience, and decision-making has received little detailed investigation. Here, we examined the relationships between reward and loss experience, affect, and decision-making in humans using a novel judgement bias task analysed with a novel computational model. We demonstrated the influence of within-task favourability on decision-making, with more risk-averse/‘pessimistic’ decisions following more positive previous outcomes and a greater current average earning rate. Additionally, individuals reporting more negative affect tended to exhibit greater risk-seeking decision-making, and, based on our model, estimated time more poorly. We also found that individuals reported more positive affective valence during periods of the task when prediction errors and offered decision outcomes were more positive. Our results thus provide new evidence that (short-term) within-task rewarding and punishing experiences determine both future decision-making and subjectively experienced affective states.

scholarly journals A model of mood as integrated advantage

2020 ◽  
Author(s):  
Daniel Bennett ◽  
Guy Davidson ◽  
Yael Niv
Keyword(s):  
Moving Average ◽  
Affective State ◽  
Optimization Theory ◽  
Functional Explanation ◽  
Prediction Errors ◽  
Science Literature ◽  
Affective Valence ◽  
Theoretical Derivation ◽  
Affective Science ◽  
And Behavior

Mood is an integrative and diffuse affective state that is thought to exert a pervasive effect on cognition and behavior. At the same time, mood itself is thought to fluctuate slowly as a product of feedback from interactions with the environment. Here we present a new computational theory of the valence of mood—the Integrated Advantage model—that seeks to account for this bidirectional interaction. Adopting theoretical formalisms from reinforcement learning, we propose to conceptualize the valence of mood as a leaky integral of an agent’s appraisals of the Advantage of its actions. This model generalizes and extends previous models of mood wherein affective valence was conceptualized as a moving average of reward prediction errors. We give a full theoretical derivation of the Integrated Advantage model and provide a functional explanation of how an integrated-Advantage variable could be deployed adaptively by a biological agent to accelerate learning in complex and/or stochastic environments. Specifically, drawing on stochastic optimization theory, we propose that an agent can utilize our hypothesized form of mood to approximate a momentum-based update to its behavioral policy, thereby facilitating rapid learning of optimal actions. We then show how this model of mood provides a principled and parsimonious explanation for a number of contextual effects on mood from the affective science literature, including expectation- and surprise-related effects, counterfactual effects from information about foregone alternatives, action-typicality effects, and action/inaction asymmetry.

scholarly journals Dissecting the links between reward and loss, decision-making, and self-reported affect using a computational approach

2020 ◽  
Author(s):  
Vikki Neville ◽  
Peter Dayan ◽  
Iain Donald Gilchrist ◽  
Elizabeth S. Paul ◽  
Michael Mendl
Keyword(s):  
Decision Making ◽  
Prediction Errors ◽  
Affective States ◽  
Affective Valence ◽  
Judgement Bias ◽  
Decision Outcomes ◽  
Loss Experience ◽  
Average Earning ◽  
Task Experience ◽  
New Evidence

Links between affective states and risk-taking are often characterised using summary statistics from serial decision-making tasks. However, our understanding of these links, and the utility of decision-making as a marker of affect, needs to accommodate the fact that ongoing (e.g. within-task) experience of rewarding and punishing decision outcomes may alter future decisions and affective states. To date, the interplay between affect, ongoing reward and punisher experience, and decision-making has received little detailed investigation. Here, we examined the relationships between reward and loss experience, affect, and decision-making in humans using a novel judgement bias task analysed with a novel computational model. We demonstrated the influence of within-task favourability on decision-making, with more risk-averse/`pessimistic' decisions following more positive previous outcomes and a greater current average earning rate. Additionally, individuals reporting more negative affect tended to exhibit greater risk-seeking decision-making, and, based on our model, estimated time more poorly. We also found that individuals reported more positive affective valence during periods of the task when prediction errors and offered decision outcomes were more positive. Our results thus provide new evidence that (short-term) within-task rewarding and punishing experiences determine both future decision-making and subjectively experienced affective states.

scholarly journals Behavioral measures of impulsivity and compulsivity in adolescents with nonsuicidal self-injury

CNS Spectrums ◽  
2021 ◽  
pp. 1-9
Author(s):  
Nina M. Lutz ◽  
Samuel R. Chamberlain ◽  
Ian M. Goodyer ◽  
Anupam Bhardwaj ◽  
Barbara J. Sahakian ◽  
...  
Keyword(s):  
Decision Making ◽  
Negative Binomial ◽  
Community Sample ◽  
Future Research ◽  
Gambling Task ◽  
Reversal Task ◽  
Behavioral Measures ◽  
Self Injury ◽  
Neurocognitive Measures ◽  
Nonsuicidal Self Injury

Abstract Background Nonsuicidal self-injury (NSSI) is prevalent among adolescents and research is needed to clarify the mechanisms which contribute to the behavior. Here, the authors relate behavioral neurocognitive measures of impulsivity and compulsivity to repetitive and sporadic NSSI in a community sample of adolescents. Methods Computerized laboratory tasks (Affective Go/No-Go, Cambridge Gambling Task, and Probabilistic Reversal Task) were used to evaluate cognitive performance. Participants were adolescents aged 15 to 17 with (n = 50) and without (n = 190) NSSI history, sampled from the ROOTS project which recruited adolescents from secondary schools in Cambridgeshire, UK. NSSI was categorized as sporadic (1-3 instances per year) or repetitive (4 or more instances per year). Analyses were carried out in a series of linear and negative binomial regressions, controlling for age, gender, intelligence, and recent depressive symptoms. Results Adolescents with lifetime NSSI, and repetitive NSSI specifically, made significantly more perseverative errors on the Probabilistic Reversal Task and exhibited significantly lower quality of decision making on the Cambridge Gambling Task compared to no-NSSI controls. Those with sporadic NSSI did not significantly differ from no-NSSI controls on task performance. NSSI was not associated with behavioral measures of impulsivity. Conclusions Repetitive NSSI is associated with increased behavioral compulsivity and disadvantageous decision making, but not with behavioral impulsivity. Future research should continue to investigate how neurocognitive phenotypes contribute to the onset and maintenance of NSSI, and determine whether compulsivity and addictive features of NSSI are potential targets for treatment.

scholarly journals Reasoning Through the Disjunctive Syllogism in Monkeys

2021 ◽  
Vol 32 (2) ◽  
pp. 292-300
Author(s):  
Stephen Ferrigno ◽  
Yiyun Huang ◽  
Jessica F. Cantlon
Keyword(s):  
Decision Making ◽  
Food Choice ◽  
Human Learning ◽  
Choice Task ◽  
Logical Inference ◽  
Critical Aspect ◽  
Disjunctive Syllogism ◽  
Symbolic Thought ◽  
Dependent Relation ◽  
Nonhuman Animals

The capacity for logical inference is a critical aspect of human learning, reasoning, and decision-making. One important logical inference is the disjunctive syllogism: given A or B, if not A, then B. Although the explicit formation of this logic requires symbolic thought, previous work has shown that nonhuman animals are capable of reasoning by exclusion, one aspect of the disjunctive syllogism (e.g., not A = avoid empty). However, it is unknown whether nonhuman animals are capable of the deductive aspects of a disjunctive syllogism (the dependent relation between A and B and the inference that “if not A, then B” must be true). Here, we used a food-choice task to test whether monkeys can reason through an entire disjunctive syllogism. Our results show that monkeys do have this capacity. Therefore, the capacity is not unique to humans and does not require language.

Emotion prediction errors guide socially adaptive behavior

2021 ◽  
Author(s):  
Joseph Heffner ◽  
Jae-Young Son ◽  
Oriel FeldmanHall
Keyword(s):  
Decision Making ◽  
Real Time ◽  
Adaptive Behavior ◽  
Emotional Response ◽  
New Method ◽  
Prediction Errors ◽  
Emotional Experiences ◽  
Past Work ◽  
Reward Prediction ◽  
Expected Outcomes

People make decisions based on deviations from expected outcomes, known as prediction errors. Past work has focused on reward prediction errors, largely ignoring violations of expected emotional experiences—emotion prediction errors. We leverage a new method to measure real-time fluctuations in emotion as people decide to punish or forgive others. Across four studies (N=1,016), we reveal that emotion and reward prediction errors have distinguishable contributions to choice, such that emotion prediction errors exert the strongest impact during decision-making. We additionally find that a choice to punish or forgive can be decoded in less than a second from an evolving emotional response, suggesting emotions swiftly influence choice. Finally, individuals reporting significant levels of depression exhibit selective impairments in using emotion—but not reward—prediction errors. Evidence for emotion prediction errors potently guiding social behaviors challenge standard decision-making models that have focused solely on reward.

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